Nationwide Prediction of Drought Conditions in Iran Based on Remote Sensing Data

• Published in: IEEE transactions on computers 2014-01, Vol.63 (1), p.90-101
• Main Authors: Jalili, Mahdi, Gharibshah, Joobin, Ghavami, Seyed Morsal, Beheshtifar, Mohammadreza, Farshi, Reza
• Format: Article
• Language: English
• Subjects: data mining; Indexes; Meteorology; Modeling; neural networks; Neurons; performance measures; prediction; Predictive models; Remote sensing; Temperature measurement; Vegetation mapping
• ISSN: 0018-9340; 1557-9956
• DOI: 10.1109/TC.2013.118

Iran is a country in a dry part of the world and extensively suffers from drought. Drought is a natural, temporary, and iterative phenomenon that is caused by shortage in rainfall, which affects people's health and well-being adversely as well as impacting the society's economy and politics with far-reaching consequences. Information on intensity, duration, and spatial coverage of drought can help decision makers to reduce the vulnerability of the drought-affected areas, and therefore, lessen the risks associated with drought episodes. One of the major challenges of modeling drought (and short-term forecasting) in Iran is unavailability of long-term meteorological data for many parts of the country. Satellite-based remote sensing dataâthat are freely availableâgive information on vegetation conditions and land cover. In this paper, we constructed artificial neural network to model (and forecast) drought conditions based on satellite imagery. To this end, standardized precipitation index (SPI) was used as a measure of drought severity. A number of features including normalized difference vegetation index (NDVI), vegetation condition index (VCI), and temperature condition index (TCI) were extracted from NOAA-AVHRR images. The model received these features as input and outputted the SPI value (or drought condition). Applying the model to the data of stations for which the precipitation data were available, we showed that it could forecast the drought condition with an accuracy of up to 90 percent. Furthermore, TCI was found to be the best marker of drought conditions among satellite-based features. We also found multilayer perceptron better than radial basis function networks and support vector machines forecasting drought conditions.